Resolving phenotypic and prognostic differences in interstitial lung disease related to systemic sclerosis by computed tomography-based radiomics

Radiomic features are quantitative data calculated from routine medical images and have shown great potential for disease phenotyping and risk stratification in cancer. Patients with systemic sclerosis (SSc), a multi-organ autoimmune disorder, have a similarly poor prognosis (10-year survival of 66%), due to interstitial lung disease (ILD) as the primary cause of death. Here, we present the analysis of 1,355 stable radiomic features extracted from computed tomography scans from 156 SSc-ILD patients, which describe distinct disease phenotypes and show prognostic power in two independent cohorts. We derive and externally validate a first quantitative radiomic risk score, qRISSc that accurately predicts progression-free survival in SSc-ILD and outperforms current clinical stratification measures. Correlation analysis with lung proteomics, histology and gene expression data in a cross-species approach demonstrates that qRISSc reverse translates into mice and captures the fibrotic remodeling process in experimental ILD.